# PILOT: Legal Case Outcome Prediction with Case Law

arXiv: https://arxiv.org/abs/2401.15770 · HTML: https://mlsanity.com/paper/2401.15770/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 34 references that are in the map.
- First posted on arXiv 28 Jan 2024.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Neural Legal Judgment Prediction in English](https://mlsanity.com/paper/1906.02059/) (2019)
- [ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation](https://mlsanity.com/paper/2105.13562/) (2021)
- [Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases](https://mlsanity.com/paper/2103.13084/) (2021)
- [A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges](https://mlsanity.com/paper/2204.04859/) (2022)
- [An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels](https://mlsanity.com/paper/2010.01653/) (2020)
- [Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions](https://mlsanity.com/paper/1802.08504/) (2018)
- [Charge-Based Prison Term Prediction with Deep Gating Network](https://mlsanity.com/paper/1908.11521/) (2019)
- [Improved Multi-label Classification under Temporal Concept Drift: Rethinking Group-Robust Algorithms in a Label-Wise Setting](https://mlsanity.com/paper/2203.07856/) (2022)
- [Concept Drift Adaptation by Exploiting Historical Knowledge](https://mlsanity.com/paper/1702.03500/) (2017)
- [Learning Fine-grained Fact-Article Correspondence in Legal Cases](https://mlsanity.com/paper/2104.10726/) (2021)
- [On the Impact of Temporal Concept Drift on Model Explanations](https://mlsanity.com/paper/2210.09197/) (2022)
- [JUSTICE: A Benchmark Dataset for Supreme Court's Judgment Prediction](https://mlsanity.com/paper/2112.03414/) (2021)
- [SECaps: A Sequence Enhanced Capsule Model for Charge Prediction](https://mlsanity.com/paper/1810.04465/) (2018)

## Built on by (most cited first, 6 of 6)

- [LawLLM: Law Large Language Model for the US Legal System](https://mlsanity.com/paper/2407.21065/) (2024)
- [CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation](https://mlsanity.com/paper/2406.17186/) (2024)
- [Evaluating the Role of Large Language Models in Legal Practice in India](https://mlsanity.com/paper/2508.09713/) (2025)
- [The Judge Variable: Challenging Judge-Agnostic Legal Judgment Prediction](https://mlsanity.com/paper/2507.13732/) (2025)
- [JBE-QA: Japanese Bar Exam QA Dataset for Assessing Legal Domain Knowledge](https://mlsanity.com/paper/2511.22869/) (2025)
- [Temporal Concept Drift in Legal Judgment Prediction: Neural Baselines Across Three Epochs of Ukrainian Court Decisions](https://mlsanity.com/paper/2605.24452/) (2026)

## Abstract (arXiv)

Machine learning shows promise in predicting the outcome of legal cases, but most research has concentrated on civil law cases rather than case law systems. We identified two unique challenges in making legal case outcome predictions with case law. First, it is crucial to identify relevant precedent cases that serve as fundamental evidence for judges during decision-making. Second, it is necessary to consider the evolution of legal principles over time, as early cases may adhere to different legal contexts. In this paper, we proposed a new framework named PILOT (PredictIng Legal case OuTcome) for case outcome prediction. It comprises two modules for relevant case retrieval and temporal pattern handling, respectively. To benchmark the performance of existing legal case outcome prediction models, we curated a dataset from a large-scale case law database. We demonstrate the importance of accurately identifying precedent cases and mitigating the temporal shift when making predictions for case law, as our method shows a significant improvement over the prior methods that focus on civil law case outcome predictions.